Data pre-processing
PhysioDataToolbox (Version 0.6.3), a MATLAB-based (MATLAB 2020b Component Runtime (v9.9)) application, was used to pre-process data from Biopac (Sjak-Shie, 2022). All data were formatted and imported into the toolbox, and then the ECG signal analyzer and HRV analyzer in the toolbox were used. The ECG signal analyzer treated the raw ECG data with a 1 Hz high-pass filter, a 50 Hz low-pass filter, and 1x signal gain. Then the ECG analyzer detected R-peak with the feature of 0.5 mV minimum R-peak, 0.3 s minimum distance between R-peak, 0.3 s minimum interbeat interval value, and 2 s maximum interbeat interval value. Baseline epochs with 5 or 10 minutes were defined according to the procedures of different experiments. After the R-peaks detection, the ECG data were visually inspected and manually corrected to remove ectopic beats, artifacts, and misidentified R-peaks singly. We extracted the root mean square of continuous heartbeat interval difference (RMSSD) to assess heart rate variability in time domains for the baseline epoch. Although some frequency-domain metrics such as high-frequency power also reflect parasympathetic activity, RMSSD is more correlated with vagal regulation and is less affected by respiratory and motor artifacts (Penttilä et al., 2001). The data from Polar, which only measures the intervals between two R-peaks in a consecutive period, were different from the data from Biopac which contains the whole heartbeat cycles and intervals. As such, we analyzed these data via Artiifact and Kubios 3.0.2 (Kaufmann et al., 2011; Tarvainen et al., 2002, 2014). The published polar data were adopted directly from three studies from our lab (Pulopulos et al., 2020a; Pulopulos et al., 2020b).